Cybersecurity Concerns in Healthcare AI: Ensuring Patient Safety and Trustworthy Adoption of AI

Summary

• The adoption of AI in the healthcare industry brings with it significant cybersecurity concerns, particularly related to adversarial machine learning. Examples of adversarial AI attacks in the healthcare domain include adversarial examples, poisoning attacks, and data theft.
• Trustworthy AI systems are critical in healthcare because they help ensure the safety and efficacy of medical treatments and services.
• Today, the technology to secure AI against such attacks and make healthcare AI systems trustworthy and responsible already exists.

As the healthcare industry increasingly adopts artificial intelligence (AI) technologies, cybersecurity issues related to AI have become a major concern. One of the main issues is adversarial machine learning, where attackers manipulate the input data to cause AI systems to make incorrect decisions. This can have serious consequences in the healthcare industry, where a misdiagnosis or incorrect treatment could put patients at risk.

Trustworthy and secure AI systems are critical in healthcare because they help ensure the safety and efficacy of medical treatments and services. In healthcare, AI systems are often used to make diagnostic decisions, suggest treatments, and monitor patient health. If these systems are not trustworthy, they may provide inaccurate or misleading information, leading to misdiagnosis or improper treatment. This can put patients at risk and undermine their trust in the healthcare system. Secure AI systems are also important for protecting the privacy of patient data. In the healthcare industry, large amounts of sensitive personal and medical information are collected, stored, and shared. If these systems are not secure, they may be vulnerable to attacks by malicious actors, who may use AI to steal or manipulate patient data.

Here are a few examples of adversarial AI attacks in the healthcare domain:

1. Adversarial examples: In this type of attack, a malicious actor adds specially crafted data to a training dataset in order to mislead a machine learning model. For example, an attacker could add an image of a benign-looking skin lesion to a dataset used to train a model to detect cancerous lesions, causing the model to misclassify the benign lesion as cancerous.
2. Poisoning attacks: In this type of attack, a malicious actor modifies the parameters of a machine learning model in order to make it less effective at detecting certain patterns. For example, an attacker could modify a model used to predict heart attack risk by adding false data to the training dataset, causing the model to underestimate the risk of heart attack in certain individuals.
3. Data theft: In this type of attack, a malicious actor uses AI to mine patient data for personal or financial gain. For example, an attacker could use machine learning algorithms to identify patterns in patient records and use that information to target individuals with scams or phishing attacks.

In conclusion, the adoption of AI in the healthcare industry brings with it significant cybersecurity concerns, particularly related to adversarial machine learning, regulation, and the need for trustworthy systems. It is essential that healthcare organizations take these issues seriously and take steps to address them in order to ensure the safe and effective use of AI in healthcare.

Way forward: Security for AI Models

So, in the current context, is there a way to secure AI systems against such attacks? AI Security technology hardens the security posture of AI systems, exposes vulnerabilities, reduces the risk of attacks on AI systems and lowers the impact of successful attacks. Important stakeholders need to adopt a set of best practices in securing systems against AI attacks, including considering attack risks and surfaces when deploying AI systems, adopting reforms in their model development and deployment workflows to make attacks difficult to execute, and creating attack response plans.

AIShield helps enterprises safeguard their AI assets powering the most important products with an extensive security platform. With its SaaS based API, AIShield provides enterprise-class AI model security vulnerability assessment and threat informed defense mechanism for wide variety of AI use cases across all industries. For more information, visit www.boschaishield.com and follow us on LinkedIn.

Additional resources on this topic

1. Article — “What are AI attacks?” by AIShield: https://boschaishield.com/blog/what-are-ai-attacks/
2. Webinar — “Cybersecurity for AI in Digital Health” by AIShield: https://boschaishield.com/blog/cybersecurity-for-ai-in-digital-health/
3. Whitepaper — “AI Security Whitepaper” by AIShield: https://boschaishield.com/resources/whitepaper/